Patterns of cross-contamination in a multispecies population genomic project: detection, quantification, impact, and solutions

FastqPuri: high-performance preprocessing of RNA-seq data

Background

RNA sequencing (RNA-seq) has become the standard means of analyzing gene and transcript expression in high-throughput. While previously sequence alignment was a time demanding step, fast alignment methods and even more so transcript counting methods which avoid mapping and quantify gene and transcript expression by evaluating whether a read is compatible with a transcript, have led to significant speed-ups in data analysis. Now, the most time demanding step in the analysis of RNA-seq data is preprocessing the raw sequence data, such as running quality control and adapter, contamination and quality filtering before transcript or gene quantification. To do so, many researchers chain different tools, but a comprehensive, flexible and fast software that covers all preprocessing steps is currently missing.

Results

We here present FastqPuri, a light-weight and highly efficient preprocessing tool for fastq data. FastqPuri provides sequence quality reports on the sample and dataset level with new plots which facilitate decision making for subsequent quality filtering. Moreover, FastqPuri efficiently removes adapter sequences and sequences from biological contamination from the data. It accepts both single- and paired-end data in uncompressed or compressed fastq files. FastqPuri can be run stand-alone and is suitable to be run within pipelines. We benchmarked FastqPuri against existing tools and found that FastqPuri is superior in terms of speed, memory usage, versatility and comprehensiveness.

Conclusions

FastqPuri is a new tool which covers all aspects of short read sequence data preprocessing. It was designed for RNA-seq data to meet the needs for fast preprocessing of fastq data to allow transcript and gene counting, but it is suitable to process any short read sequencing data of which high sequence quality is needed, such as for genome assembly or SNV (single nucleotide variant) detection. FastqPuri is most flexible in filtering undesired biological sequences by offering two approaches to optimize speed and memory usage dependent on the total size of the potential contaminating sequences. FastqPuri is available at https://github.com/jengelmann/FastqPuri. It is implemented in C and R and licensed under GPL v3.

Electronic supplementary material

The online version of this article (10.1186/s12859-019-2799-0) contains supplementary material, which is available to authorized users.

From trash to treasure: detecting unexpected contamination in unmapped NGS data

Background

Next Generation Sequencing (NGS) experiments produce millions of short sequences that, mapped to a reference genome, provide biological insights at genomic, transcriptomic and epigenomic level. Typically the amount of reads that correctly maps to the reference genome ranges between 70% and 90%, leaving in some cases a consistent fraction of unmapped sequences. This ’misalignment’ can be ascribed to low quality bases or sequence differences between the sample reads and the reference genome. Investigating the source of the unmapped reads is definitely important to better assess the quality of the whole experiment and to check for possible downstream or upstream ’contamination’ from exogenous nucleic acids.

Results

Here we propose DecontaMiner, a tool to unravel the presence of contaminating sequences among the unmapped reads. It uses a subtraction approach to identify bacteria, fungi and viruses genome contamination. DecontaMiner generates several output files to track all the processed reads, and to provide a complete report of their characteristics. The good quality matches on microorganism genomes are counted and compared among samples. DecontaMiner builds an offline HTML page containing summary statistics and plots. The latter are obtained using the state-of-the-art D3 javascript libraries. DecontaMiner has been mainly used to detect contamination in human RNA-Seq data. The software is freely available at http://www-labgtp.na.icar.cnr.it/decontaminer.

Conclusions

DecontaMiner is a tool designed and developed to investigate the presence of contaminating sequences in unmapped NGS data. It can suggest the presence of contaminating organisms in sequenced samples, that might derive either from laboratory contamination or from their biological source, and in both cases can be considered as worthy of further investigation and experimental validation. The novelty of DecontaMiner is mainly represented by its easy integration with the standard procedures of NGS data analysis, while providing a complete, reliable, and automatic pipeline.

Electronic supplementary material

The online version of this article (10.1186/s12859-019-2684-x) contains supplementary material, which is available to authorized users.

Defending Our Public Biological Databases as a Global Critical Infrastructure

Progress in modern biology is being driven, in part, by the large amounts of freely available data in public resources such as the International Nucleotide Sequence Database Collaboration (INSDC), the world's primary database of biological sequence (and related) information. INSDC and similar databases have dramatically increased the pace of fundamental biological discovery and enabled a host of innovative therapeutic, diagnostic, and forensic applications. However, as high-value, openly shared resources with a high degree of assumed trust, these repositories share compelling similarities to the early days of the Internet. Consequently, as public biological databases continue to increase in size and importance, we expect that they will face the same threats as undefended cyberspace. There is a unique opportunity, before a significant breach and loss of trust occurs, to ensure they evolve with quality and security as a design philosophy rather than costly "retrofitted" mitigations. This Perspective surveys some potential quality assurance and security weaknesses in existing open genomic and proteomic repositories, describes methods to mitigate the likelihood of both intentional and unintentional errors, and offers recommendations for risk mitigation based on lessons learned from cybersecurity.

Inferring Ancient Relationships with Genomic Data: A Commentary on Current Practices

Contemporary phylogeneticists enjoy an embarrassment of riches, not only in the volumes of data now available, but also in the diversity of bioinformatic tools for handling these data. Here, I discuss a subset of these tools I consider well-suited to the task of inferring ancient relationships with coding sequence data in particular, encompassing data generation, orthology assignment, alignment and gene tree inference, supermatrix construction, and analysis under the best-fitting models applicable to large-scale datasets. Throughout, I compare and critique methods, considering both their theoretical principles and the details of their implementation, and offering practical tips on usage where appropriate. I also entertain different motivations for analyzing what are almost always originally DNA sequence data as codons, amino acids, and higher-order recodings. Although presented in a linear order, I see value in using the diversity of tools available to us to assess the sensitivity of clades of biological interest to different gene and taxon sets and analytical modes, which can be an indication of the presence of systematic error, of which a few forms remain poorly controlled by even the best available inference methods.

Codon Usage Bias in Animals: Disentangling the Effects of Natural Selection, Effective Population Size, and GC-Biased Gene Conversion

Selection on codon usage bias is well documented in a number of microorganisms. Whether codon usage is also generally shaped by natural selection in large organisms, despite their relatively small effective population size (Ne), is unclear. In animals, the population genetics of codon usage bias has only been studied in a handful of model organisms so far, and can be affected by confounding, nonadaptive processes such as GC-biased gene conversion and experimental artefacts. Using population transcriptomics data, we analyzed the relationship between codon usage, gene expression, allele frequency distribution, and recombination rate in 30 nonmodel species of animals, each from a different family, covering a wide range of effective population sizes. We disentangled the effects of translational selection and GC-biased gene conversion on codon usage by separately analyzing GC-conservative and GC-changing mutations. We report evidence for effective translational selection on codon usage in large-Ne species of animals, but not in small-Ne ones, in agreement with the nearly neutral theory of molecular evolution. C- and T-ending codons tend to be preferred over synonymous G- and A-ending ones, for reasons that remain to be determined. In contrast, we uncovered a conspicuous effect of GC-biased gene conversion, which is widespread in animals and the main force determining the fate of AT↔GC mutations. Intriguingly, the strength of its effect was uncorrelated with Ne.

Influence of Recombination and GC-biased Gene Conversion on the Adaptive and Nonadaptive Substitution Rate in Mammals versus Birds

Recombination is expected to affect functional sequence evolution in several ways. On the one hand, recombination is thought to improve the efficiency of multilocus selection by dissipating linkage disequilibrium. On the other hand, natural selection can be counteracted by recombination-associated transmission distorters such as GC-biased gene conversion (gBGC), which tends to promote G and C alleles irrespective of their fitness effect in high-recombining regions. It has been suggested that gBGC might impact coding sequence evolution in vertebrates, and particularly the ratio of nonsynonymous to synonymous substitution rates (dN/dS). However, distinctive gBGC patterns have been reported in mammals and birds, maybe reflecting the documented contrasts in evolutionary dynamics of recombination rate between these two taxa. Here, we explore how recombination and gBGC affect coding sequence evolution in mammals and birds by analyzing proteome-wide data in six species of Galloanserae (fowls) and six species of catarrhine primates. We estimated the dN/dS ratio and rates of adaptive and nonadaptive evolution in bins of genes of increasing recombination rate, separately analyzing AT → GC, GC → AT, and G ↔ C/A ↔ T mutations. We show that in both taxa, recombination and gBGC entail a decrease in dN/dS. Our analysis indicates that recombination enhances the efficiency of purifying selection by lowering Hill-Robertson effects, whereas gBGC leads to an overestimation of the adaptive rate of AT → GC mutations. Finally, we report a mutagenic effect of recombination, which is independent of gBGC.

Host-derived population genomics data provides insights into bacterial and diatom composition of the killer whale skin

Recent exploration into the interactions and relationship between hosts and their microbiota has revealed a connection between many aspects of the host's biology, health and associated micro-organisms. Whereas amplicon sequencing has traditionally been used to characterize the microbiome, the increasing number of published population genomics data sets offers an underexploited opportunity to study microbial profiles from the host shotgun sequencing data. Here, we use sequence data originally generated from killer whale Orcinus orca skin biopsies for population genomics, to characterize the skin microbiome and investigate how host social and geographical factors influence the microbial community composition. Having identified 845 microbial taxa from 2.4 million reads that did not map to the killer whale reference genome, we found that both ecotypic and geographical factors influence community composition of killer whale skin microbiomes. Furthermore, we uncovered key taxa that drive the microbiome community composition and showed that they are embedded in unique networks, one of which is tentatively linked to diatom presence and poor skin condition. Community composition differed between Antarctic killer whales with and without diatom coverage, suggesting that the previously reported episodic migrations of Antarctic killer whales to warmer waters associated with skin turnover may control the effects of potentially pathogenic bacteria such as Tenacibaculum dicentrarchi. Our work demonstrates the feasibility of microbiome studies from host shotgun sequencing data and highlights the importance of metagenomics in understanding the relationship between host and microbial ecology.

A phylogenomic approach to reconstruct interrelationships of main clupeocephalan lineages with a critical discussion of morphological apomorphies

BACKGROUND

Previous molecular studies on the phylogeny and classification of clupeocephalan fishes revealed numerous new taxonomic entities. For re-analysing these taxa, we perform target gene capturing and subsequent next generation sequencing of putative ortholog exons of major clupeocephalan lineages. Sequence information for the RNA bait design was derived from publicly available genomes of bony fishes. Newly acquired sequence data comprising > 800 exon sequences was subsequently used for phylogenetic reconstructions.

RESULTS

Our results support monophyletic Otomorpha comprising Alepocephaliformes. Within Ostariophysi, Gonorynchiformes are sister to a clade comprising Cypriniformes, Characiformes, Siluriformes and Gymnotiformes, where the interrelationships of Characiformes, Siluriformes and Gymnotiformes remain enigmatic. Euteleosts comprise four major clades: Lepidogalaxiiformes, Protacanthopterygii, Stomiatii, and Galaxiiformes plus Neoteleostei. The monotypic Lepidogalaxiiformes form the sister-group to all remaining euteleosts. Protacanthopterygii, comprising Argentini-, Esoci- and Salmoniformes, is sister to Stomiatii (Osmeriformes and Stomiatiformes) and Galaxiiformes plus Neoteleostei.

CONCLUSIONS

Several proposed monophyla defined by morphological apomorphies within the Clupeocephalan phylogeny are confirmed by the phylogenetic estimates presented herein. However, other morphologically described groups cannot be reconciled with molecular phylogenies. Thus, numerous morphological apomoprhies of supposed monophyla are called into question. The interpretation of suggested morphological synapomorphies of otomorph fishes is strongly affected by the inclusion of deep-sea inhabiting, and to that effect morphologically adapted Alepocephaliformes. Our revision of these potential synapomorphies, in the context that Alepocephaliformes are otomorph fishes, reveals that only a single character of the total nine characters proposed as synapomorphic for the group is clearly valid for all otomorphs. Three further characters remain possible apomorphies since their status remains unclear in the deep-sea adapted Alepocephaliformes showing developmental lag and lacking a swim bladder. Further, our analysis places Galaxiiformes as sister group to neoteleosts, which contradicts some previous molecular phylogenetic studies. This needs further investigation from a morphological perspective, as suggested synapomophies for several euteleostean lineages are challenged or still lacking. For the verification of results presented herein, a denser phylogenomic-level taxon sampling should be applied.

Consensus assessment of the contamination level of publicly available cyanobacterial genomes

Publicly available genomes are crucial for phylogenetic and metagenomic studies, in which contaminating sequences can be the cause of major problems. This issue is expected to be especially important for Cyanobacteria because axenic strains are notoriously difficult to obtain and keep in culture. Yet, despite their great scientific interest, no data are currently available concerning the quality of publicly available cyanobacterial genomes. As reliably detecting contaminants is a complex task, we designed a pipeline combining six methods in a consensus strategy to assess the contamination level of 440 genome assemblies of Cyanobacteria. Two methods are based on published reference databases of ribosomal genes (SSU rRNA 16S and ribosomal proteins), one is indirectly based on a reference database of marker genes (CheckM), and three are based on complete genome analysis. Among those genome-wide methods, Kraken and DIAMOND blastx share the same reference database that we derived from Ensembl Bacteria, whereas CONCOCT does not require any reference database, instead relying on differences in DNA tetramer frequencies. Given that all the six methods appear to have their own strengths and limitations, we used the consensus of their rankings to infer that >5% of cyanobacterial genome assemblies are highly contaminated by foreign DNA (i.e., contaminants were detected by 5 or 6 methods). Our results will help researchers to check the quality of publicly available genomic data before use in their own analyses. Moreover, we argue that journals should make mandatory the submission of raw read data along with genome assemblies in order to facilitate the detection of contaminants in sequence databases.

Metagenomic sequencing suggests a diversity of RNA interference-like responses to viruses across multicellular eukaryotes

RNA interference (RNAi)-related pathways target viruses and transposable element (TE) transcripts in plants, fungi, and ecdysozoans (nematodes and arthropods), giving protection against infection and transmission. In each case, this produces abundant TE and virus-derived 20-30nt small RNAs, which provide a characteristic signature of RNAi-mediated defence. The broad phylogenetic distribution of the Argonaute and Dicer-family genes that mediate these pathways suggests that defensive RNAi is ancient, and probably shared by most animal (metazoan) phyla. Indeed, while vertebrates had been thought an exception, it has recently been argued that mammals also possess an antiviral RNAi pathway, although its immunological relevance is currently uncertain and the viral small RNAs (viRNAs) are not easily detectable. Here we use a metagenomic approach to test for the presence of viRNAs in five species from divergent animal phyla (Porifera, Cnidaria, Echinodermata, Mollusca, and Annelida), and in a brown alga-which represents an independent origin of multicellularity from plants, fungi, and animals. We use metagenomic RNA sequencing to identify around 80 virus-like contigs in these lineages, and small RNA sequencing to identify viRNAs derived from those viruses. We identified 21U small RNAs derived from an RNA virus in the brown alga, reminiscent of plant and fungal viRNAs, despite the deep divergence between these lineages. However, contrary to our expectations, we were unable to identify canonical (i.e. Drosophila- or nematode-like) viRNAs in any of the animals, despite the widespread presence of abundant micro-RNAs, and somatic transposon-derived piwi-interacting RNAs. We did identify a distinctive group of small RNAs derived from RNA viruses in the mollusc. However, unlike ecdysozoan viRNAs, these had a piRNA-like length distribution but lacked key signatures of piRNA biogenesis. We also identified primary piRNAs derived from putatively endogenous copies of DNA viruses in the cnidarian and the echinoderm, and an endogenous RNA virus in the mollusc. The absence of canonical virus-derived small RNAs from our samples may suggest that the majority of animal phyla lack an antiviral RNAi response. Alternatively, these phyla could possess an antiviral RNAi response resembling that reported for vertebrates, with cryptic viRNAs not detectable through simple metagenomic sequencing of wild-type individuals. In either case, our findings show that the antiviral RNAi responses of arthropods and nematodes, which are highly divergent from each other and from that of plants and fungi, are also highly diverged from the most likely ancestral metazoan state.

Identification and Removal of Contaminant Sequences From Ribosomal Gene Databases: Lessons From the Census of Deep Life

Earth’s subsurface environment is one of the largest, yet least studied, biomes on Earth, and many questions remain regarding what microorganisms are indigenous to the subsurface. Through the activity of the Census of Deep Life (CoDL) and the Deep Carbon Observatory, an open access 16S ribosomal RNA gene sequence database from diverse subsurface environments has been compiled. However, due to low quantities of biomass in the deep subsurface, the potential for incorporation of contaminants from reagents used during sample collection, processing, and/or sequencing is high. Thus, to understand the ecology of subsurface microorganisms (i.e., the distribution, richness, or survival), it is necessary to minimize, identify, and remove contaminant sequences that will skew the relative abundances of all taxa in the sample. In this meta-analysis, we identify putative contaminants associated with the CoDL dataset, recommend best practices for removing contaminants from samples, and propose a series of best practices for subsurface microbiology sampling. The most abundant putative contaminant genera observed, independent of evenness across samples, were Propionibacterium, Aquabacterium, Ralstonia, and Acinetobacter. While the top five most frequently observed genera were Pseudomonas, Propionibacterium, Acinetobacter, Ralstonia, and Sphingomonas. The majority of the most frequently observed genera (high evenness) were associated with reagent or potential human contamination. Additionally, in DNA extraction blanks, we observed potential archaeal contaminants, including methanogens, which have not been discussed in previous contamination studies. Such contaminants would directly affect the interpretation of subsurface molecular studies, as methanogenesis is an important subsurface biogeochemical process. Utilizing previously identified contaminant genera, we found that ∼27% of the total dataset were identified as contaminant sequences that likely originate from DNA extraction and DNA cleanup methods. Thus, controls must be taken at every step of the collection and processing procedure when working with low biomass environments such as, but not limited to, portions of Earth’s deep subsurface. Taken together, we stress that the CoDL dataset is an incredible resource for the broader research community interested in subsurface life, and steps to remove contamination derived sequences must be taken prior to using this dataset.

Tumour virology in the era of high-throughput genomics

With the advent of massively parallel sequencing, oncogenic viruses in tumours can now be detected in an unbiased and comprehensive manner. Additionally, new viruses or strains can be discovered based on sequence similarity with known viruses. Using this approach, the causative agent for Merkel cell carcinoma was identified. Subsequent studies using data from large collections of tumours have confirmed models built during decades of hypothesis-driven and low-throughput research, and a more detailed and comprehensive description of virus-tumour associations have emerged. Notably, large cohorts and high sequencing depth, in combination with newly developed bioinformatical techniques, have made it possible to rule out several suggested virus-tumour associations with a high degree of confidence. In this review we discuss possibilities, limitations and insights gained from using massively parallel sequencing to characterize tumours with viral content, with emphasis on detection of viral sequences and genomic integration events.This article is part of the themed issue 'Human oncogenic viruses'.

A software tool 'CroCo' detects pervasive cross-species contamination in next generation sequencing data

Background

Multiple RNA samples are frequently processed together and often mixed before multiplex sequencing in the same sequencing run. While different samples can be separated post sequencing using sample barcodes, the possibility of cross contamination between biological samples from different species that have been processed or sequenced in parallel has the potential to be extremely deleterious for downstream analyses.

Results

We present CroCo, a software package for identifying and removing such cross contaminants from assembled transcriptomes. Using multiple, recently published sequence datasets, we show that cross contamination is consistently present at varying levels in real data. Using real and simulated data, we demonstrate that CroCo detects contaminants efficiently and correctly. Using a real example from a molecular phylogenetic dataset, we show that contaminants, if not eliminated, can have a decisive, deleterious impact on downstream comparative analyses.

Conclusions

Cross contamination is pervasive in new and published datasets and, if undetected, can have serious deleterious effects on downstream analyses. CroCo is a database-independent, multi-platform tool, designed for ease of use, that efficiently and accurately detects and removes cross contamination in assembled transcriptomes to avoid these problems. We suggest that the use of CroCo should become a standard cleaning step when processing multiple samples for transcriptome sequencing.

Electronic supplementary material

The online version of this article (10.1186/s12915-018-0486-7) contains supplementary material, which is available to authorized users.

Phylogeny with introgression in Habronattus jumping spiders (Araneae: Salticidae)

Background

Habronattus is a diverse clade of jumping spiders with complex courtship displays and repeated evolution of Y chromosomes. A well-resolved species phylogeny would provide an important framework to study these traits, but has not yet been achieved, in part because the few genes available in past studies gave conflicting signals. Such discordant gene trees could be the result of incomplete lineage sorting (ILS) in recently diverged parts of the phylogeny, but there are indications that introgression could be a source of conflict.

Results

To infer Habronattus phylogeny and investigate the cause of gene tree discordance, we assembled transcriptomes for 34 Habronattus species and 2 outgroups. The concatenated 2.41 Mb of nuclear data (1877 loci) resolved phylogeny by Maximum Likelihood (ML) with high bootstrap support (95-100%) at most nodes, with some uncertainty surrounding the relationships of H. icenoglei, H. cambridgei, H. oregonensis, and Pellenes canadensis. Species tree analyses by ASTRAL and SVDQuartets gave almost completely congruent results. Several nodes in the ML phylogeny from 12.33 kb of mitochondrial data are incongruent with the nuclear phylogeny and indicate possible mitochondrial introgression: the internal relationships of the americanus and the coecatus groups, the relationship between the altanus, decorus, banksi, and americanus group, and between H. clypeatus and the coecatus group. To determine the relative contributions of ILS and introgression, we analyzed gene tree discordance for nuclear loci longer than 1 kb using Bayesian Concordance Analysis (BCA) for the americanus group (679 loci) and the VCCR clade (viridipes/clypeatus/coecatus/roberti groups) (517 loci) and found signals of introgression in both. Finally, we tested specifically for introgression in the concatenated nuclear matrix with Patterson’s D statistics and D_FOIL. We found nuclear introgression resulting in substantial admixture between americanus group species, between H. roberti and the clypeatus group, and between the clypeatus and coecatus groups.

Conclusions

Our results indicate that the phylogenetic history of Habronattus is predominantly a diverging tree, but that hybridization may have been common between phylogenetically distant species, especially in subgroups with complex courtship displays.

Electronic supplementary material

The online version of this article (10.1186/s12862-018-1137-x) contains supplementary material, which is available to authorized users.

The virome of Drosophila suzukii, an invasive pest of soft fruit

Drosophila suzukii (Matsumura) is one of the most damaging and costly pests to invade temperate horticultural regions in recent history. Conventional control of this pest is challenging, and an environmentally benign microbial biopesticide is highly desirable. A thorough exploration of the pathogens infecting this pest is not only the first step on the road to the development of an effective biopesticide, but also provides a valuable comparative dataset for the study of viruses in the model family Drosophilidae. Here we use a metatransciptomic approach to identify viruses infecting this fly in both its native (Japanese) and invasive (British and French) ranges. We describe eighteen new RNA viruses, including members of the Picornavirales, Mononegavirales, Bunyavirales, Chuviruses, Nodaviridae, Tombusviridae, Reoviridae, and Nidovirales, and discuss their phylogenetic relationships with previously known viruses. We also detect 18 previously described viruses of other Drosophila species that appear to be associated with D. suzukii in the wild.